GestureX: Master Hand Command for Robotic Precision

by aniketmandal in Circuits > Arduino

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GestureX: Master Hand Command for Robotic Precision

Screenshot 2024-08-31 150517.png
Screenshot 2024-08-31 150517.png

### The Story Behind GestureX: Master Hand Command for Robotic Precision


In a world increasingly driven by automation and human-machine collaboration, the idea for GestureX was born out of a need to make robotic interactions more intuitive, accessible, and human-like. The inspiration came from a group of engineering students who noticed a gap in how people interacted with technology—specifically, robotics. They observed that most robotic systems required complex programming or cumbersome interfaces that were not user-friendly, especially for those without technical backgrounds.


One of the students, fascinated by how effortlessly humans communicate through gestures, thought: "What if we could control a robot just by moving our hands?" This simple question sparked a wave of curiosity and excitement among the group. They envisioned a future where robots could be controlled naturally, with gestures that felt as intuitive as waving hello or pointing.


Drawing from their expertise in electronics, computer vision, and machine learning, the team began developing a prototype. They decided to use an Arduino board to control a robotic arm made of simple materials like cardboard—proving that innovation doesn't always require expensive resources. The key component of their system was a hand-tracking module that could precisely interpret hand gestures in real-time using a webcam and computer vision algorithms powered by Mediapipe.


The team faced several challenges along the way. They had to fine-tune the hand detection algorithms to minimize latency and maximize accuracy. They also worked on a seamless integration between the software and hardware, ensuring that every gesture translated into a precise movement of the robotic arm. After countless hours of coding, testing, and refining, they finally had a working prototype.


They named their creation **GestureX**, reflecting its potential to revolutionize how humans interact with robots. GestureX is not just a project; it's a vision of a world where technology adapts to us, not the other way around. It symbolizes a future where the boundary between humans and machines blurs, making technology more accessible and user-friendly.


As GestureX evolves, the team envisions its application in various fields—from assisting surgeons in operating rooms to helping people with disabilities regain independence through intuitive robotic aids. This project is just the beginning of a journey to bridge the gap between human intent and robotic action, making interaction as natural as waving a hand.

Downloads

WhatsApp Video 2024-08-31 at 13.05.37_e739953b.mp4

Supplies

https://github.com/Aniket1234-cool/GestureX-Master-Hand-Command-for-Robotic-Precision.git

Introduction

Welcome to this exciting project where we'll build a gesture-controlled robotic arm using Arduino! This project combines the power of computer vision with the versatility of Arduino to create a robotic arm that you can control with simple hand movements. Whether you're a beginner looking to dive into the world of robotics or an experienced maker seeking a fun weekend project, this Instructable is for you.

[Insert a cover image of your completed robotic arm here]

In this project, we'll be using:

  1. An Arduino Uno
  2. Two servo motors
  3. A webcam for gesture recognition
  4. Python for computer vision processing
  5. Arduino IDE for programming the microcontroller

Let's get started!

Before we begin, make sure you have all the necessary components and tools. Here's what you'll need:

Components:

  1. Arduino Uno
  2. 2 x Servo Motors (e.g., SG90 or similar)
  3. Webcam (built-in laptop camera or USB webcam)
  4. Jumper wires
  5. Breadboard
  6. USB cable (for Arduino)
  7. Robotic arm frame (3D printed or purchased kit)

Tools:

  1. Computer with Arduino IDE installed
  2. Python 3.x installed (with OpenCV and PySerial libraries)
  3. Screwdriver set
  4. Wire stripper/cutter (optional)


mermaid-diagram-2024-08-31-154629.png

Before we begin, make sure you have all the necessary components and tools. Here's what you'll need:

Components:

  1. Arduino Uno
  2. 2 x Servo Motors (e.g., SG90 or similar)
  3. Webcam (built-in laptop camera or USB webcam)
  4. Jumper wires
  5. Breadboard
  6. USB cable (for Arduino)
  7. Robotic arm frame (3D printed or purchased kit)

Tools:

  1. Computer with Arduino IDE installed
  2. Python 3.x installed (with OpenCV and PySerial libraries)
  3. Screwdriver set
  4. Wire stripper/cutter (optional)


Now, let's program the Arduino to control the servos:

  1. Open the Arduino IDE on your computer.
  2. Create a new sketch and paste the following code:
cpp
Copy
#include <Servo.h>

Servo servo1;
Servo servo2;

void setup() {
servo1.attach(9);
servo2.attach(10);
Serial.begin(9600);
}

void loop() {
if (Serial.available() > 1) {
int angle1 = Serial.read();
int angle2 = Serial.read();
servo1.write(angle1);
servo2.write(angle2);
}
}
  1. Connect your Arduino to your computer via USB.
  2. Select the correct board and port in the Arduino IDE.
  3. Click the "Upload" button to send the code to your Arduino.


For gesture recognition, we'll use Python with OpenCV:

  1. Install Python 3.x if you haven't already.
  2. Open a terminal or command prompt and install the required libraries:
Copy
pip install opencv-python pyserial

Step 6: Create the Python Script for Gesture Recognition

Create a new Python file named gesture_control.py and add the following code:

python
Copy
import cv2
import numpy as np
import serial
import time

# Initialize the webcam
cap = cv2.VideoCapture(0)

# Initialize serial connection to Arduino
arduino = serial.Serial('COM3', 9600) # Change 'COM3' to your Arduino port
time.sleep(2) # Allow time for connection to establish

def get_hand_center(frame):
# Convert to HSV color space
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# Define range of skin color in HSV
lower_skin = np.array([0, 20, 70], dtype=np.uint8)
upper_skin = np.array([20, 255, 255], dtype=np.uint8)
# Create a binary mask
mask = cv2.inRange(hsv, lower_skin, upper_skin)
# Find contours
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if contours:
# Find the largest contour (assumed to be the hand)
max_contour = max(contours, key=cv2.contourArea)
M = cv2.moments(max_contour)
if M["m00"] != 0:
cx = int(M["m10"] / M["m00"])
cy = int(M["m01"] / M["m00"])
return (cx, cy)
return None

while True:
ret, frame = cap.read()
if not ret:
break
hand_center = get_hand_center(frame)
if hand_center:
cx, cy = hand_center
# Map hand position to servo angles
angle1 = int(np.interp(cx, [0, frame.shape[1]], [0, 180]))
angle2 = int(np.interp(cy, [0, frame.shape[0]], [0, 180]))
# Send angles to Arduino
arduino.write(bytes([angle1, angle2]))
# Draw circle at hand center
cv2.circle(frame, (cx, cy), 5, (0, 255, 0), -1)
cv2.imshow('Gesture Control', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break

cap.release()
cv2.destroyAllWindows()
arduino.close()

Make sure to change 'COM3' to the correct port for your Arduino.

Run the Project

  1. Ensure your Arduino is connected and the code is uploaded.
  2. Run the Python script:
Copy
python gesture_control.py
  1. A window should open showing the webcam feed. Move your hand in front of the camera to control the robotic arm!

[Insert a video demonstration of the arm responding to hand gestures]

Step 8: Troubleshooting and Tips

  1. If the arm doesn't respond, check all your connections and ensure the correct COM port is selected in the Python script.
  2. Adjust the HSV color range in the Python script if hand detection is not working well in your lighting conditions.
  3. Experiment with different mapping functions in the Python script to achieve more intuitive control.